CN108961397B - Tree-oriented three-dimensional point cloud data simplification method - Google Patents
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Abstract
The invention provides a tree-oriented three-dimensional point cloud data simplifying method, which constructs a three-dimensional cubic grid, calculates homogeneous connection weights of the network and neighbor grids, and sequentially eliminates some redundant points according to the size of the homogeneous connection weights so as to simplify the three-dimensional point cloud data of trees. The method simplifies the data and maintains the vegetation structure expressed by the three-dimensional point cloud data of the original tree as much as possible, and has higher practical application value in the field of tree three-dimensional modeling.
Description
Technical Field
The invention belongs to the technical field of space positioning, remote sensing and communication network fusion, and particularly relates to a tree-oriented three-dimensional point cloud data simplifying method.
Background
At present, the three-dimensional scanning of objects can be rapidly carried out through equipment such as a laser scanner and a laser radar, and the equipment obtains point cloud data formed by a large number of three-dimensional space points. The point cloud data can be used for quickly and cheaply carrying out three-dimensional modeling on the object, and has important practical application significance.
However, intensive space scanning brings fine object detail information and also brings great calculation burden and storage burden, so it is necessary to simplify three-dimensional point cloud data obtained by devices such as a laser scanner and a laser radar, and to reduce the number of three-dimensional space points in a point cloud data set as much as possible while retaining the characteristics of an original object. The simplification of the three-dimensional point cloud data is very important for obtaining three-dimensional objects with less data volume and easier calculation and display.
Current methods and techniques focus on: and (3) finding out the rules of the plane and the curved surface in the object by a clustering algorithm and the partition search of the octree, and deleting or combining redundant and repeated points on the same plane and the curved surface while finding out the rules. These techniques are effective in simplifying more regular man-made buildings, man-made objects, objects with large-area planar structures. However, when a tree is processed, because the leaves and branches of the tree are included, it is difficult to find the surface and curved surface rules on the objects, and some relatively important branches and overall shape information are discarded by forced clustering and octree segmentation, so that the efficiency of the current algorithm for simplifying the tree three-dimensional point cloud data is low.
The invention aims to provide a simplification method of three-dimensional point cloud data facing coniferous trees, which can simplify the number of three-dimensional space points of the tree point cloud according to the characteristics of the tree point cloud.
The invention content is as follows:
aiming at the problems in the prior art, the invention provides a tree-oriented three-dimensional point cloud data simplifying method, which comprises the steps of constructing a three-dimensional cubic grid, calculating the homogeneous connection weight of the network and a neighbor grid, and sequentially eliminating some redundant points according to the size of the homogeneous connection weight to simplify the three-dimensional point cloud data of trees.
The invention relates to a tree-oriented three-dimensional point cloud data simplifying method, which comprises the following steps:
s1, inputting a tree point cloud data set PointDataSet containing n three-dimensional space points, inputting a grid side length width, inputting a homogenization bunching number clusternum, and inputting a simplification percentage percent:
s101, inputting a tree point cloud data set PointDataSet, wherein the PointDataSet is a data set { p) formed by three-dimensional space points1,p2,..pnThe data set comprises n three-dimensional points; any three-dimensional point pi is { px, py, pz }, px is the coordinate of the point on an x axis, py is the coordinate of the point on a y axis, and pz is the coordinate of the point on a z axis;
s102, counting all points in the PointDataSet to obtain the maximum value xmax of an x coordinate, the minimum value xmin of the x coordinate, the maximum value ymax of a y coordinate, the minimum value ymin of the y coordinate, the maximum value zmax of a z coordinate and the minimum value zmin of the z coordinate of all points in the PointDataSet;
s103, setting the x-axis span xdistance to xmax-xmin, the y-axis span ydistance to ymax-ymin and the z-axis span zmax-zmin of all points in the PointDataSet;
s104, inputting grid side length width and inputting homogenization bunching number clusternum;
the default value of the grid side length width is xdistance/1000;
the default value of the homogenization bunching number clusternum is 100;
s2, cutting a three-dimensional space occupied by the PointDataSet into a plurality of cubic grids according to the side length of width to form a cubic grid list cube:
s201, a cubic grid list CubeList is an empty set;
s202, x iterator xcount ═ xmin;
s203, y iterator ycounter ymin;
s204, the z iterator zcounter ═ zmin;
s205, establishing a cubic grid Cube, wherein the Cube comprises the following attributes:
the scope of Cube is from (xcounter, ycounter, zcounter) to (xcounter + width, ycoounter + width, zcounter + width)
Coordinates of the position of the center point of Cube:
cp＝(xcounter+width/2,ycounter+width/2,ycounter+width/2)
the point density of Cube is 0; cube belongs to category 0; the homogeneous connection weight priority of Cube is 0;
s206, the point density of Cube is the number of all spatial points included in the Cube spatial range in the PointDataSet;
s207, adding the Cube into the Cube List;
S208，zcounter＝zcounter+width；
s209, go to S210 if zcount > ═ zmax, otherwise go to S205;
S210，ycounter＝ycounter+width；
s211, if ycounter > -ymax, go to 212, otherwise go to S204;
S212，xcounter＝xcounter-1；
s213, if xcount > -xmax, go to S214, otherwise go to S203;
s214, the calculation process is ended;
s3, calculating the generic relationship category of each Cube in the Cube list to obtain a distance index:
s301, using the point density dense of each Cube in the Cube List as input data, dividing the input data into cluster categories through a k-means algorithm, and obtaining cluster centers c 1, c 2 and … c cluster; wherein c 1 represents the clustering center with number 1, c 2 represents the clustering center with number 2, and c [ clusternum ] represents the clustering center with number clusternum; c [ i ] represents the ith cluster center;
s302, for each Cube in the Cube List, calculating the distance between the density of the Cube and cluster centers, finding the cluster center closest to the density, and storing the serial number of the cluster center in the category relationship category of the Cube;
s303, establishing an array CloseDistance as an empty array;
s304, for each three-dimensional space point in the PointDataSet, finding another three-dimensional space point closest to the point and adding the distance value into the closeDistance;
s305, calculating the mean value avgdistacne of the CloseDistance;
s307, counting the average value avgcatalog of the categories of all the cubes of the Cube list;
s308, obtaining the distance index distanceindex, wherein the calculation formula is as follows:
s4, calculating the priority of each Cube homogeneous connection weight in the Cube List:
s401, processing the following steps for each Cube in the Cube list;
s402, taking out a Cube from the Cube List when the currentCube of the grid to be processed is currently processed;
s403, coordinate cp of center point position of center point currentCube to be processed currently;
s404, calculating the distances from cp of all cubes in the Cube List to the centerpoint, and taking out all cubes with the distances less than or equal to the distanceindex;
s405, where allnum is the number of cubes in the neighborlist;
s406, the homogenization counter scouter is 0, the heterogeneous counter ycounter is 0, and the iteration counter is 1;
s407, taking out the second counter Cube in the neighborlist;
s408, diff ═ ABS (subordination category of currentCube-subordination category of testCube);
wherein ABS represents the calculated absolute value;
s409, if diff is greater than 2, ycounter +1, otherwise scounter + 1;
S410，counter＝counter+1；
s411, if the counter is larger than allnum, turning to S412, otherwise, turning to S405;
s412, calculating a homogeneous connection authority pri, wherein the corresponding formula is as follows:
s413.currentCube, wherein the homogeneity of currentCube is connected with the weight priority to pri;
s5, continuously eliminating the three-dimensional space points from the PointDataSet according to the homogeneous connection weight value until the simplification percentage is reached:
s501, std is the standard deviation of the homogeneity connection weight priority of all the cubes in the Cube List;
s502, a cycle value circle is std/clusternum;
s503, sorting all the cubes of the Cube List from small to large according to the priority values;
s504, taking out the first-ranked Cube in the Cube List from the minimum grid Firstcube to be processed;
s505, taking out all three-dimensional space points contained in the FirstCube space range in the pointDataSet;
s506, if the number of the ptlist midpoints is less than 8, turning to S507, otherwise, turning to S508;
s507, deleting the FirstCube from the CubeList, and turning to S510;
s508, finding a point nearest to a coordinate cp of the position of the center point of the FirstCube in the ptlist, and deleting the point from the PointDataSet;
s509, the homogeneous connection weight priority of the FirstCube is equal to the homogeneous connection weight priority of the FirstCube plus circle;
s510, calculating the number of the three-dimensional space points currently contained in the PointDataSet according to currentN;
s511, if (n-current/n) is less than percent, go to S503, otherwise go to S512;
s512, outputting the PointDataSet as a simplification result.
The invention has the following beneficial effects: the method comprises the steps of constructing a three-dimensional cubic grid, calculating homogeneous connection weights of the network and neighbor grids, sequentially eliminating redundant points according to the homogeneous connection weights, and simplifying three-dimensional point cloud data of trees. The method simplifies the data and maintains the vegetation structure expressed by the three-dimensional point cloud data of the original tree as much as possible, and has higher practical application value in the field of tree three-dimensional modeling.
Drawings
FIG. 1 is a diagram of a tree composed of three-dimensional spatial points according to example 2;
FIG. 2 is a simplified three-dimensional pine tree view obtained by the method of the present invention in example 2;
FIG. 3 is a tree diagram composed of three-dimensional spatial points according to example 3;
FIG. 4 is a simplified three-dimensional pine tree diagram obtained by the method of the present invention in example 4.
Detailed Description
Specific embodiments of the present invention are described by way of the following examples, but it will be understood by those skilled in the art that this is by way of illustration only, the scope of the present invention is defined by the appended claims, and various changes or modifications which fall within the scope of the present invention may be made to these embodiments by those skilled in the art without departing from the principle and spirit of the present invention.
Example 1
1. A tree-oriented three-dimensional point cloud data simplification method comprises the following steps:
s1, inputting a tree point cloud data set PointDataSet containing n three-dimensional space points, inputting a grid side length width, inputting a homogenization bunching number clusternum, and inputting a simplification percentage percent:
s101, inputting a tree point cloud data set PointDataSet, wherein the PointDataSet is a data set { p) formed by three-dimensional space points1,p2,..pnThe data set comprises n three-dimensional points; any three-dimensional point pi is { px, py, pz }, px is the coordinate of the point on an x axis, py is the coordinate of the point on a y axis, and pz is the coordinate of the point on a z axis;
s102, counting all points in the PointDataSet, and obtaining the maximum value xmax of x coordinates, the minimum value xmin of x coordinates, the maximum value ymax of y coordinates, the minimum value ymin of y coordinates, the maximum value zmax of z coordinates and the minimum value zmin of z coordinates of all points in the PointDataSet;
s103, setting the x-axis span xdistance to xmax-xmin, the y-axis span ydistance to ymax-ymin and the z-axis span zmax-zmin of all points in the PointDataSet;
s104, inputting the grid side length width and the homogenization bunching number clusternum;
the default value of the grid side length width is xdistance/1000;
the default value of the homogenization bunching number clusternum is 100;
s2, cutting the three-dimensional space occupied by the PointDataSet into a plurality of cubic grids according to the side length width to form a cubic grid list cube:
s201, a cubic grid list CubeList is an empty set;
s202, an x iterator xcount ═ xmin;
s203, y iterator ycounter ymin;
s204, the z iterator zcounter ═ zmin;
s205, establishing a cubic grid Cube, wherein the Cube comprises the following attributes:
cube corresponds to a spatial range scope from (xcount, ycounter, zcounter) to (xcount + width, ycounter + width, zcounter + width)
Coordinates of the position of the center point of Cube:
cp＝(xcounter+width/2,ycounter+width/2,ycounter+width/2)
the dot density of Cube is 0; cube belongs to category 0; the homogeneous connection weight priority of Cube is 0;
s206, the point density of Cube is the number of all spatial points included in the Cube spatial range in the PointDataSet;
s207, adding the Cube into the Cube List;
S208，zcounter＝zcounter+width；
s209, go to S210 if zcount > ═ zmax, otherwise go to S205;
S210，ycounter＝ycounter+width；
s211, if ycounter > -ymax, go to 212, otherwise go to S204;
S212，xcounter＝xcounter-1；
s213, if xcount > -xmax, go to S214, otherwise go to S203;
s214, the calculation process is ended;
s3, calculating the generic relationship category of each Cube in the Cube list to obtain a distance index:
s301, using the point density dense of each Cube in the Cube List as input data, dividing the input data into cluster categories through a k-means algorithm, and obtaining cluster centers c 1, c 2 and … c cluster; wherein c 1 represents the clustering center with number 1, c 2 represents the clustering center with number 2, and c [ clusternum ] represents the clustering center with number clusternum; c [ i ] represents the ith cluster center;
s302, for each Cube in the Cube List, calculating the distance between the density of the Cube and cluster centers, finding the cluster center closest to the density, and storing the serial number of the cluster center in the category relationship category of the Cube;
s303, establishing an array CloseDistance as an empty array;
s304, for each three-dimensional space point in the PointDataSet, finding another three-dimensional space point closest to the point and adding the distance value into the CloseDistance;
s305, calculating the mean value avgdistacne of the CloseDistance;
s307, counting the average value avgcatalog of the categories of all the cubes of the Cube list;
s308, obtaining the distance index distanceindex, wherein the calculation formula is as follows:
s4, calculating the priority of each Cube homogeneous connection weight in the Cube List:
s401, processing the following steps for each Cube in the Cube list;
s402, taking out a Cube from the Cube List when the currentCube of the grid to be processed is currently processed;
s403, coordinate cp of center point position of center point currentCube to be processed currently;
s404, calculating the distances from cp of all cubes in the Cube List to the centerpoint, and taking out all cubes with the distances less than or equal to the distanceindex;
s405, where allnum is the number of cubes in the neighborlist;
s406, the homogenization counter scouter is 0, the heterogeneous counter ycounter is 0, and the iteration counter is 1;
s407, taking out the second Cube in the neighbor borlist;
s408, diff ═ ABS (subordination category of currentCube-subordination category of testCube);
wherein ABS represents the calculated absolute value;
s409, if diff is greater than 2, ycounter +1, otherwise scoounter + 1;
S410，counter＝counter+1；
s411, if the counter is larger than allnum, turning to S412, otherwise, turning to S405;
s412, calculating a homogeneous connection authority pri, wherein the corresponding formula is as follows:
s413, the homogeneity of currentCube is connected with the weight priority pri;
s5, continuously eliminating the three-dimensional space points from the PointDataSet according to the homogeneous connection weight value until the simplification percentage is reached:
s501, std is the standard deviation of the homogeneity connection weight priority of all the cubes in the Cube List;
s502, a cycle value circle is std/clusternum;
s503, sorting all the cubes of the Cube List from small to large according to the priority values;
s504, taking out the first-ranked Cube in the Cube List from the minimum grid Firstcube to be processed;
s505, taking out all three-dimensional space points contained in the FirstCube space range in the pointDataSet;
s506, if the number of the middle points of the ptlist is less than 8, turning to S507, otherwise, turning to S508;
s507, deleting the FirstCube from the CubeList, and turning to S510;
s508, finding a point nearest to a coordinate cp of the position of the center point of the FirstCube in the ptlist, and deleting the point from the PointDataSet;
s509, the homogeneous connection weight priority of the FirstCube is equal to the homogeneous connection weight priority of the FirstCube plus circle;
s510, calculating the number of the three-dimensional space points currently contained in the PointDataSet according to currentN;
s511, if (n-current)/n is less than percent, then go to S503, otherwise go to S512;
s512, outputting the PointDataSet as a simplification result.
Example 2
A tree (pinus koraiensis) consisting of three-dimensional points:
step 1, inputting a three-dimensional pine tree formed by point clouds as shown in figure 1; the PointDataSet of fig. 1 contains 100 ten thousand three-dimensional points, the side length width of the input grid is 5, the homogenization bunch number is 30, and the simplification percentage is 50%;
step 2, cube grid list cube list;
step 3, calculating to obtain a distance index 0.7432;
step 4, calculating the priority of each Cube homogeneous connection weight in the Cube list;
step 5, obtaining a simplified result, as shown in fig. 2; the number of three-dimensional space points is greatly reduced compared with the number in the original three-dimensional point cloud picture, the original form of the tree is still ensured, and the method is relatively effective.
Example 3
Using a tree consisting of three-dimensional points according to example 1 of the invention (Nardostachys)
Step 1, inputting a three-dimensional pine tree formed by point clouds, as shown in fig. 3; the PointDataSet of fig. 3 contains 30 ten thousand three-dimensional points, the side length width of the input grid is 4, the homogenization bunch number is 25, and the simplification percentage is 50%;
step 2, cube grid list cube list;
step 3, calculating to obtain a distance index distanceindex which is 0.621;
step 4, calculating the priority of each Cube homogeneous connection weight in the Cube list;
step 5, the simplified result is obtained, as shown in fig. 4, which shows that the simplification and the morphology of the tree are still preserved.
Claims (1)
1. A tree-oriented three-dimensional point cloud data simplification method comprises the following steps:
s1, inputting a tree point cloud data set PointDataSet containing n three-dimensional space points, inputting a grid side length width, inputting a homogenization bunching number clusternum, and inputting a simplification percentage:
s101, inputting a tree point cloud data set PointDataSet, wherein the PointDataSet is a data set { p) formed by three-dimensional space points1, p2, …, pnAny three-dimensional point pi = { px, py, pz }, px is the coordinate of the point on the x-axis, py is the coordinate of the point on the y-axis, and pz is the coordinate of the point on the z-axis;
s102, counting all points in the PointDataSet to obtain the maximum value xmax of an x coordinate, the minimum value xmin of the x coordinate, the maximum value ymax of a y coordinate, the minimum value ymin of the y coordinate, the maximum value zmax of a z coordinate and the minimum value zmin of the z coordinate of all points in the PointDataSet;
s103, x-axis span xdistance = xmax-xmin, y-axis span ydistance = ymax-ymin, and z-axis span zdistance = zmax-zmin of all points in the PointDataSet;
s104, inputting the grid side length width and the homogenization bunching number clusternum;
the default value of the grid side length width is xdistance/1000;
the default value of the homogenization bunching number clusternum is 100;
s2, cutting the three-dimensional space occupied by the PointDataSet into a plurality of cubic grids according to the side length width, and forming a cubic grid list cube:
s201, a cubic grid list CubeList = empty set;
s202, x iterator xcount = xmin;
s203, y iterator ycounter = ymin;
s204, z iterator zcounter = zmin;
s205, establishing a cubic grid Cube, wherein the Cube comprises the following attributes:
the spatial range scope corresponding to Cube is from (xcount, ycounter, zcounter) to (xcount + width, ycounter + width, zcounter + width);
coordinates of the position of the center point of Cube:
cp=(xcounter+width/2,ycounter+width/2,ycounter+width/2)；
cube point density = 0; cube's membership category = 0; cube's homogenous connection weight priority = 0;
s206, Cube point density = the number of all spatial points included in the Cube spatial range in the PointDataSet;
s207, adding the Cube into the Cube List;
S208，zcounter=zcounter+width；
s209, go to S210 if zcount > = zmax, otherwise go to S205;
S210，ycounter=ycounter+width；
s211, if ycounter > = ymax, go to 212, otherwise go to S204;
S212，xcounter=xcounter-1；
s213, go to S214 if xcount > = xmax, otherwise go to S203;
s214, the calculation process is ended;
s3, calculating the generic relationship category of each Cube in the Cube list to obtain the distance index:
s301, using the point density dense of each Cube in the Cube List as input data, dividing the input data into cluster categories through a k-means algorithm, and obtaining cluster centers c 1, c 2 and … c cluster;
wherein c 1 represents the clustering center with number 1, c 2 represents the clustering center with number 2, and c [ clusternum ] represents the clustering center with number clusternum; c [ i ] denotes the ith cluster center;
s302, for each Cube in the Cube List, calculating the distance between the density of the Cube and cluster centers, finding the cluster center closest to the density, and storing the serial number of the cluster center in the category relationship category of the Cube;
s303, establishing an array CloseDistance = null array;
s304, for each three-dimensional space point in the PointDataSet, finding another three-dimensional space point closest to the point and adding the distance value into the closeDistance;
s305, calculating the mean value avgdistacne of the CloseDistance;
s307, counting the average value avgcatalog of the categories of all the cubes of the Cube list;
s308, obtaining the distance index distanceindex, wherein the calculation formula is as follows:
s4, calculating the priority of each Cube homogeneous connection weight in the Cube List:
s401, processing the following steps for each Cube in the Cube list;
s402, taking out one Cube in the Cube List when the currentCube = the grid to be processed currently;
s403, the coordinate cp of the center point position of the center point center = current cube to be processed currently;
s404, calculating the distances from cp of all Cubes in the CubeList to centerpoint, and taking out all Cubes with the distances less than or equal to distanceindex;
s405, allnum = number of cubes in neighborist;
s406, the homogenization counter scount =0, the heterogeneous counter yzcount =0, and the iteration counter count = 1;
s407, testCube = take out the second counter Cube in neighborlist;
s408, diff = ABS (membership category of currentCube-membership category of testCube);
wherein ABS represents the calculated absolute value;
s409, yzcount = yzcount +1 if diff is greater than 2, otherwise scouter = scouter + 1;
S410，counter=counter+1；
s411, if the counter is larger than allnum, turning to S412, otherwise, turning to S405;
s412, calculating a homogeneous connection authority pri, wherein the corresponding formula is as follows:
s413. currentCube' S homogeneous weight priority = pri;
s5, continuously eliminating the three-dimensional space points from the PointDataSet according to the homogeneous connection weight value until the simplification percentage is reached:
s501, std = calculating the standard deviation of the homogeneous connection weight priority of all the cubes in the Cube List;
s502, a rotation value circle = std/clusternum;
s503, sorting all the cubes of the Cube List from small to large according to the priority values;
s504, taking out the first-ranked Cube in the Cube List, wherein the first-ranked Cube is the minimum grid Firstcube = to be processed;
s505, taking out all three-dimensional space points contained in the FirstCube space range within the pointDataSet by ptlist =;
s506, if the number of the ptlist midpoints is less than 8, turning to S507, otherwise, turning to S508;
s507, deleting the FirstCube from the CubeList, and turning to S510;
s508, finding a point nearest to a coordinate cp of the position of the center point of the FirstCube in the ptlist, and deleting the point from the PointDataSet;
s509, the homogeneous connection weight priority of the FirstCube = the homogeneous connection weight priority + circle of the FirstCube;
s510, currentN = calculating the number of the points of the PointDataSet which currently contain the three-dimensional space;
s511, if (n-current/n) is less than percent, go to S503, otherwise go to S512;
s512, outputting the PointDataSet as a simplification result.
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